Beyond RAG: Implementing Agent Search with LangGraph for Smarter Knowledge Retrieval
Beyond RAG: Implementing Agent Search with LangGraph for Smarter Knowledge Retrieval
Agent Search is live today! https://lnkd.in/g5RjsXMQ Traditional search stops at documents. Agent Search goes further: ✅ Breaks down complex questions into sub-queries ✅ Synthesizes insights from multiple sources ✅ Delivers contextual, actionable answers
We just shipped AI Agents for deep research over all your company’s knowledge. After talking to users, we’ve discovered two main problems with the existing solutions. 1. The times when users need a search tool the most, they don’t know how to describe the documents in the perfect terms for a single search to be able to find it. 2. Many of the more interesting questions at work don’t break down into a simple search. For example, if I want to compare the sales process done for two customers, I need to deeply research the process for each and the resulting outcomes. So we’ve built an open source deep research system over all connected sources (think Google Drive, Slack, Salesforce, Confluence, etc.) that can break down the user’s question and iteratively dive deeper into the directions that it finds promising as it’s researching. And it doesn’t just stop there. Onyx's AI agent analyzes its own chain-of-thought and answers to ensure that there are no unsupported claims, contradictions, or missing parts in the final answer. Watch me walk through the feature below 👇